Saransh Chopra

selected publications

1. A new SymPy backend for Vector: Uniting experimental and theoretical physicists

   Chopra, Saransh, and Pivarski, Jim

   EPJ Web Conf. 2025

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   Vector is a Python library for 2D, 3D, and Lorentz vectors, especially arrays of vectors, to solve common physics problems in a NumPy-like way. Vector can currently perform numerical computations, and through this paper, we introduce a new symbolic backend that extends Vector’s utility to theoretical physicists. The numerical backends of Vector enable users to create pure Python object, NumPy arrays, and Awkward arrays of vectors. The object and Awkward backends are also implemented in Numba to leverage Just-In- Time (JIT) compiled vector calculations. The new symbolic backend, built on top of SymPy expressions, showcases Vector’s ability to support far-flung cases and allows SymPy methods and functions to work on vector classes. Moreover, apart from a few software, high energy physics has maintained a strict separation between tools used by theorists and experimentalists, and Vector’s SymPy backend aims to bridge this gap, providing a unified computational framework for both communities.

2. Vector: JIT-compilable mathematical manipulations of ragged Lorentz vectors

   Chopra, Saransh, Schreiner, Henry, Rodrigues, Eduardo, Eschle, Jonas, and Pivarski, Jim

   Journal of Open Source Software 2025

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   Mathematical manipulation of vectors is a crucial component of data analysis pipelines in high energy physics, enabling physicists to transform raw data into meaningful results that can be visualized. More specifically, high energy physicists work with 2D and 3D Euclidean vectors, and 4D Lorentz vectors that can be used as physical quantities, such as position, momentum, and forces. Given that high energy physics data are not uniform, vector manipulation frameworks or libraries are expected to work readily on non-uniform or ragged data, data with variable-sized rows (or a nested data structure with variable-sized entries); thus, the library is expected to perform operations on an entire ragged structure in minimum passes. Furthermore, optimizing memory usage and processing time has become essential with the increasing computational demands at the Large Hadron Collider (LHC), the world’s largest particle accelerator. Vector is a Python library for creating and manipulating 2D, 3D, and Lorentz vectors, especially arrays of vectors, to solve common physics problems in a NumPy-like (Harris et al., 2020) way. The library enables physicists to operate on high energy physics data in a high level language without compromising speed. The library is already in use at LHC and is a part of frameworks, like Coffea (Gray et al., 2023), employed by physicists across multiple high energy physics experiments.

3. Predicting efficacy of antiseizure medication treatment with machine learning algorithms in North Indian population

   Kaushik, Mahima, Mahajan, Siddhartha, Machahary, Nitin, Thakran, Sarita, Chopra, Saransh, Tomar, Raj Vardhan, Kushwaha, Suman S., Agarwal, Rachna, Sharma, Sangeeta, Kukreti, Ritushree, and Biswal, Bibhu

   Epilepsy Research 2024

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   Purpose This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE). Methods Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into ‘good responder (GR)’ and ‘poor responder (PR)’ based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models. Results Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework. Significance Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient’s response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier’s predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients.